多雲環境中雲代理商獲利最佳化與近似公平之市場模型

A Broker-Centric Multi-Cloud Marketplace Model for Profit Optimization with Near Minimax Fairness

潘荏羽(Jen-Yu Pan)

指導教授 ： 蔡志宏

國立臺灣大學/電機資訊學院/電信工程學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202204111

摘要

近幾年來，雲端處理的服務顯著增加。雲端計算的一個重要特徵是現付現拿。然而，受限於傳統雲端運算的市場結構，雲使用者支付的費用往往高於實際使用量，而且無法滿足雲使用者。因此，為了解決雲端運算所面臨的問題，許多研究探討如何結合一個新的參與者 - 雲代理商以更滿足典型的雲使用者的需求。雲代理商定期從雲端以批發價租用一些預留的虛擬器，並以低於雲端提供的標準價的價格提供給雲使用者。此外，在這個市場中，與雲供應商相比，雲代理商通常可採用更短的計費周期，因此雲代理商可以為雲使用者降低大量時間成本。
然而，大多數以雲代理商為中心的研究都集中在如何提高市場表現，例如產生更多利潤或最小化成本消耗。在這些研究中很少有人考慮到真實市場的限制，即市場中雲供應商的虛擬器數量有限。由於雲供應商的虛擬器昂貴且有限，雲代理商將接收大量雲使用者的需求，尤其是在大型市場中。
此篇論文在以雲代理商為中心的市場架構下，我們提出一種新的以雲代理商為中心的虛擬器分配演算法。為了解決虛擬器數量有限的問題，我們採用所謂的信用評分規則和定價規則來決定虛擬器分別由雲供應商和雲代理商租出和購買的數量和價格。雲代理商的運作又可以分為兩種模式：以利潤為目標的模式和在以公平為目標的模式。利潤目標模式側重於如何配置虛擬器以及如何為其定價，以為雲代理商之用戶節省成本的前提下最大化其利潤，其中雲代理商的利潤受許多因素影響，像是使用者的需求、虛擬器的買賣價格、市場的規模等等。另一個公平目標模式側重於滿足用戶需求，在這種模式下，雲代理商的目標不是關注自身的利潤，而是要在使用者的虛擬器分配上實現公平。
此篇論文以雲代理商模型實作市場架構，並評估論文所提出的演算法在不同環境下的效能。結果顯示，論文提出的這種新的以雲代理商為中心的多雲環境被證明是有效的，即使在使用者需求波動很大的多雲環境下也是如此。此外，提出的虛擬器分配演算法可以有效地為使用者降低成本，在資源短缺的情況下有效地保持公平，同時通過不同的商業運作模式獲得可觀的利潤。

關鍵字

雲端計算 ； 運算資源分配 ； 經濟學 ； 虛擬器 ； 最佳化

並列摘要

Over the past few years, cloud computing has experienced remarkable development. One important feature of cloud computing is pay-as-you-go. However, users often pay more than their actual usage due to the current cloud market structure. In this situation, traditional cloud providers may not able to satisfy the QoS of end users because of high price. Therefore, we introduce a new stakeholder to better meet typical cloud user demands, which is the service broker in the cloud market. A service broker periodically leases a number of reserved instances from cloud providers with a whole-sale price and then offers them to users on an on-demand basis at a price cheaper than the standard price of cloud providers. Also, in this market the broker usually adopts a shorter billing cycle compared with cloud providers. By doing this, the broker can reduce a great amount of cost for users.
However, most broker-centric researches focused on how to improve the performance of the market such as generating more profit or minimizing cost consumption. Few of them considered the physical limitation in the real market, that is, the limited number of instances of cloud providers in Broker-Centric Market. That is, because the number of instances in cloud providers is expensive and limited, service broker will accommodate a large number of end users especially in large scale marketplace.
In this thesis, we propose a novel broker-centric instance-allocating algorithm for Broker-Centric marketplace. I n order to address the problem of limited number of instances, we adopt the so-called credit rules and pricing rules to determine the number and the price of instances to lease and purchase by cloud providers and service brokers respectively. The business operation of this service broker can further be divided into two modes: the profit-objective mode and the fairness-objective mode. The profit-objective mode focuses on how to configure a broker and how to price its instances such that its profit can be maximized on the premise of saving costs for users. Profit of a broker is affected by many factors such as the user demands, the purchase price, the sales price of instances, and the scale of the broker, etc. The fairness-objective mode, in addition, is activated when the total number of instances is too less to meet the users’ demands, and the broker wants to satisfy user requirements as much as it can. In this mode, instead of focusing on the profit of the broker, the objective of the broker is to achieve minimax fairness on instance allocation for user applications to assure virtual machines resources are fairly allocated.
In the final part of this thesis, the performance of Broker-Centric marketplace and Broker-Centric instance-allocating algorithm are validated via a series of experiments. This new broker-centric cloud market system is proved to be effective even when working on a large-scale cloud marketplace even with high fluctuation of users’ demands. Moreover, this system also shows that the broker can efficiently reduce the cost for users, and effectively maintain fairness upon resource shortage, while still making a considerable profit via different modes of business operations.

並列關鍵字

Cloud computing ； Pricing ； Economics ； Virtual machine ； Optimization

參考文獻

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